In machine control system design, it is important to utilize resources to their maximum capacities in order to optimize system throughput and/or minimize cost associated with such optimization. In the case of printers, photocopiers, and the like, conventional control mechanisms are limited in the manner in which they process information, perform jobs or tasks, plan such jobs or tasks, etc. For instance, where multiple tasks are to be performed for a job, a specific ordering of tasks may be preferable to the exclusion of one or more other orderings, and such a solution may be considered optimal for execution by a machine. In the case of a print platform, for example, an optimal ordering of pages to be printed, print associated tasks, or the like can be desirable in order to mitigate resource waste and improve printer throughput.
Conventional systems and methods for planning employ various search algorithms, including an A* search algorithm, which typically operates to facilitate finding a path from a given starting state to a desired goal state. An A* search algorithm is an example of a “best-first” search algorithm because it utilizes a heuristic estimate to rank a given node according to an estimate of an optimal route passing through the node to the desired node. The search algorithm may then determine an order in which to evaluate nodes according to their respective estimated values. However, A* algorithms explore all nodes that cannot be proven to be worse than the optimal, which can be time consuming and computationally expensive. This in turn can occupy resources (e.g., memory, processing power, etc.) and lead to processing delays. Accordingly, an unmet need exists in the art for systems and/or methods that facilitate improved processing efficiency for search algorithms and associated data structures.